A platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of ADCs and clinical evaluation of thrombocytopenia



  • Predicting clinical ADC efficacy and toxicity is a challenge. The mAb, linker, and payload all need to be optimized for a particular tumor target, indication, and patient population.
  • Two mechanistic models were developed using preclinical Trastuzumab-emtansine (T-DM1) data to project clinical efficacy and tumor reduction and thrombocytopenia (TCP) incidence, a common ADC adverse event.
  • The models were built to be generalizable for the study of any ADC.
  • Combining efficacy and toxicity models allows us to explore common therapeutic metrics, such as therapeutic window and indexes.


  • Clinical outcomes of ADCs can be projected with this mechanistic platform model.
    • The efficacy model was validated with T-DXd in mBC patients, which successfully reproduced clinical PK, efficacy, and PSF.
  • Mechanistic TCP model can capture interpatient variability.
  • The models were built to be generalizable to other ADCs by substituting relevant parameters.

Future directions: 

  • Continue validating efficacy model on other ADCs with different indications.
  • Use TCP rates from clinical data with higher doses to improve predictions.
  • Determine whether the mechanistic TCP model can be used for preclinical to clinical translation and use more dose data for training.


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